Multi Task Inverse Reinforcement Learning for Common Sense Reward

Understand

One of the challenges in applying reinforcement learning in a complex real-world environment lies in providing the agent with a sufficiently detailed reward function.

• Any misalignment between the reward and the desired behavior can result in unwanted outcomes.
• This may lead to issues like "reward hacking" where the agent maximizes rewards by unintended behavior.
• In this work, we propose to disentangle the reward into two distinct parts.